MARTINI_enrich_BERTopic_DiedSuddenlyConnecticut

This is a BERTopic model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.

Usage

To use this model, please install BERTopic:

pip install -U bertopic

You can use the model as follows:

from bertopic import BERTopic
topic_model = BERTopic.load("AIDA-UPM/MARTINI_enrich_BERTopic_DiedSuddenlyConnecticut")

topic_model.get_topic_info()

Topic overview

• Number of topics: 15
• Number of training documents: 1737

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	danbury - stamford - vaccinated - rn - july	21	-1_danbury_stamford_vaccinated_rn
0	naugatuck - mary - carolee - terryville - nurse	1012	0_naugatuck_mary_carolee_terryville
1	chemotherapy - lymphoblastic - diagnosed - megan - marrow	116	1_chemotherapy_lymphoblastic_diagnosed_megan
2	danbury - robert - steven - navy - moynihan	91	2_danbury_robert_steven_navy
3	norwalk - teachers - kristin - stafford - vaccinated	81	3_norwalk_teachers_kristin_stafford
4	uconn - vaccinated - snhu - colleges - unitedhealth	61	4_uconn_vaccinated_snhu_colleges
5	vaers - vaccinated - myocarditis - deaths - 65	59	5_vaers_vaccinated_myocarditis_deaths
6	firefighters - norwalk - responder - lieutenant - tracy	54	6_firefighters_norwalk_responder_lieutenant
7	micheal - larose - dahill - wajnowski - eversource	47	7_micheal_larose_dahill_wajnowski
8	defibrillator - icu - strokes - blockage - cody	46	8_defibrillator_icu_strokes_blockage
9	vaccinated - pfizer - shots - darien - toddlers	40	9_vaccinated_pfizer_shots_darien
10	vaccinating - clinic - pratt - hhc - fema	33	10_vaccinating_clinic_pratt_hhc
11	david - wethersfield - buckley - eisenberg - idaho	32	11_david_wethersfield_buckley_eisenberg
12	glioblastoma - neurosurgeon - migraines - stamford - andrew	23	12_glioblastoma_neurosurgeon_migraines_stamford
13	quinnipiac - hamden - dr - sheila - vaccination	21	13_quinnipiac_hamden_dr_sheila

Training hyperparameters

• calculate_probabilities: True
• language: None
• low_memory: False
• min_topic_size: 10
• n_gram_range: (1, 1)
• nr_topics: None
• seed_topic_list: None
• top_n_words: 10
• verbose: False
• zeroshot_min_similarity: 0.7
• zeroshot_topic_list: None

Framework versions

• Numpy: 1.26.4
• HDBSCAN: 0.8.40
• UMAP: 0.5.7
• Pandas: 2.2.3
• Scikit-Learn: 1.5.2
• Sentence-transformers: 3.3.1
• Transformers: 4.46.3
• Numba: 0.60.0
• Plotly: 5.24.1
• Python: 3.10.12